Latent Space: The AI Engineer Podcast
00:53:22 · Shawn Wang, Jamin Ball
[00:53:22] S1: They've seen it all. [00:53:22] S0: And they've seen it all, and they were highly skeptical. And sometimes you have industry vets that are highly skeptical, and they miss the next big opportunity because they never think the incumbent can be disrupted. But the point of view they had was it's very different to tape out a chip that is competitive with the leading chips at the time from building a machine that can continuously tape out chips that compete with, know, [00:53:49] S1: It's the Marvel Marvel factory. [00:53:50] S0: Yeah. The chip factory. And so he was like, this individual's point of view was maybe a chip startup can build one chip, but guess what? They're not gonna have the machine to constantly pump out a new chip every year and stay at the frontier from the chip standpoint. And that's why NVIDIA's king. It's not because they have the best chip now, it's they have the best factory. Process for chips. Exactly. Exactly. [00:54:09] S1: It's the machine that builds the machine that
00:54:09 · Shawn Wang, Jamin Ball
[00:54:09] S1: It's the machine that builds the machine that [00:54:11] S0: And so it's kind of one of these... I mean, this is the bear case on all these Neo Labs, which is, you and and you talk to many smart folks, what they might tell you is, hey. If you wanna really compete and be... Have like a soda model at the frontier, you can't you can't you can't go more than one order of magnitude of compute less than the labs. You just can't compete if you don't have, you know, one order of magnitude of compute of the big labs. And so as a neolab, you know, the question isn't can you build one model? It's can you attract compute and talent required to continuously pump out new generations of models every year to ultimately compete in your domain? And so I think for the labs, the reality is is I think it's very hard [00:54:55] S1: for
00:54:55 · Jamin Ball
[00:54:55] S0: anyone, unknown players, to catch up to kind of like the generalized frontier big models. I think you'll continue to see domain specific models or models or like new types of models, decision models like JEV. But I think the think the big labs, they are on the treadmill of continuously pumping out new models. Because guess what? If if Anthropic stops, OpenAI and Gemini will continue. And then you'll just kind of see revenue... You'll see revenue fall off, which is why I do think it's so important to stay at the frontier. Now what do I expect? And like, what have I seen from Anthropic now in the data? It's as their model families get better and better, they get more efficient, but they also capture more revenue. That could be because they're doing more economically valuable work or they're doing the work faster. But what you're seeing from now kind of black and white is, hey, as Anthropic has 2x'd their compute spend to build and train these models, they've 14x'd their revenue. And I'm not really sure what would cause that trend to stop. I think models are only gonna take on longer running tasks, more economically valuable tasks. And if you can't tell, I'm a hyper optimistic person, and so maybe I'm just drinking the Kool Aid too much. But I I do think we're nowhere close to hitting a limit of the dollar kind of like economic value that leading models can capture. I think what's maybe more interesting is, you know, OpenAI, you know, Luna class models, kind of also trying to capture and compete at the low end of the market with much smaller, much cheaper models, you know, and kind of span everything from Frontier to
00:56:49 · Shawn Wang, Jamin Ball
[00:56:49] S1: Small. [00:56:50] S0: Yeah. Distilled versions of their own models that are smaller and and, you know, cents per million in put output tokens, not dollars. [00:56:58] S1: Yep. Cosign all of that. I think that is a good way to think about it. I I I think, you know, fun... I'll just comment this because it's in my head. Six one Terra has been pretty pretty successful. Six one Luna. Like, that that whole series is very interesting. The approaches of the different labs is also super interesting. Anthropic to this day has no decision model. Mhmm. And they're like, well, we're good. Like, we we use TypeSave. Whatever. Yeah. Yeah. Yeah. And, like, well, they don't have a stand alone voice API. Right. They they they have a voice model. They just don't don't market it. Right. They don't... They've never done images. Right. Know, it's it's a it's a lot of enterprise focus and coding focus. Yes. And OpenAI has been a little bit more of, like, we'll do everything and Yeah. We'll try Sora. Shut down Sora. Yes. Remember when Sora was, like, the number one app [00:57:41] S0: in Yeah. Exactly. [00:57:42] S1: In The US. [00:57:43] S0: Yeah. Exactly.
00:57:43 · Jamin Ball, Shawn Wang
[00:57:43] S0: Yeah. Exactly. [00:57:44] S1: Yeah. And then Gemini has been the most... Like, we will explore everything. And so, like, we appreciate them because if they didn't try it, no one else would. Yeah. Right? Like, they have the funds for it. Yeah. But, like, I think often about Anthropic in terms of, like, well, okay, enterprise encoding. And they... They're first to it. They nailed it. They still continue to nail it. Right? Everyone's chasing after them. Yep. Is that the story going forward? Is is is it just more of the same, or, like, how... What should we expect? [00:58:10] S0: I think it's... I think a lot of this boils down to, like, a new type of innovator's dilemma, which I almost call, like, the compute innovator's dilemma. Like, let's say you're OpenAI, and you have Sora. You have many different model families. You have image models. You have everything. Well, the reality is is how do you choose what to serve? Maybe SORA demand explodes. Are you gonna allocate a lot of your codex compute [00:58:36] S1: To slot videos? [00:58:37] S0: Yeah. Because you probably will have to. Like, if you actually wanna make [00:58:40] S1: I mean, that's exactly why they shut down SORA.
00:58:40 · Shawn Wang, Jamin Ball
[00:58:40] S1: I mean, that's exactly why they shut down SORA. [00:58:41] S0: Yeah. Experience good. You need to. And at the end of the day, what a lot of people have decided is, well, guess what? A lot of the highest ROI, most economically valuable work, has been in coding. So it's hard to prioritize anything else other than that. It's why there's probably opportunities for other companies and startups to build models and build products in those domains because guess what? What is open ad, Anthropic chasing? They're not chasing the incremental $10,000,000,000 market cap. They're chasing the next trillion. And so the reality is for a startup or a neolab, great. You could go build a company, build a product, build a model where you exit for $10,000,000,000 and it's a home run. Grand slam. That's awesome. And simultaneously, it's too small for OpenAI and Anthropic to matter. And so like, what is Anthropic done just a bang up job at? They said coding is our thing. We're gonna be great at coding. We're gonna build the best coding harness in Claude Code, and we're gonna continue to nail it. I think everyone is kind of starting to ask themselves, well, when do we... Maybe you asked a version of this earlier. When do we tap out the coding TAM? I think one point of view is [00:59:41] S1: I have asked very many times this question.